Personalized handwriting recognition via biased regularization

ACM International Conference Proceeding Series

Volumn 148, Issue , 2006, Pages 457-464

  Kienzle, Wolf ^a,b   Chellapilla, Kumar ^a  

^a MICROSOFT RESEARCH   (United States)

^b MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE SYSTEMS; DATA STRUCTURES; KNOWLEDGE ENGINEERING; SUPPORT VECTOR MACHINES; USER INTERFACES;

BIASED REGULARIZATION; GENERIC RECOGNIZER; HANDWRITING RECOGNITION; REGULARIZATION TERM;

CHARACTER RECOGNITION;

EID: 34250770037     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1143844.1143902     Document Type: Conference Paper

References (14)

1. Brakensiek, A., Kosmala, A., & Rigoll, G. (2001). Comparing adaptation techniques for on-line handwriting recognition. Sixth International Conference on Document Analysis and Recognition (pp. 486-490).
2. Chang, C. C., & Lin, C. J. (2001). LIBSVM: a library for support vector machines. Available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
3. Connell, S. D., & Jain, A. K. (2002). Writer adaptation for online handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 329-346.
4. Friess, T., Cristianini, N., & Campbell, C. (1998). The kernel adatron algorithm: a fast and simple learning procedure for support vector machine. Proceedings of the 15th International Conference on Machine Learning. Morgan Kaufman.
5. Hsu, C. W., & Lin, C. J. (2002). A comparison on methods for multi-class support vector machines. IEEE Transactions on Neural Networks, 13, 415-425.
6. Kimeldorf, G. S., & Wahba, G. (1971). Some results on Tchebycheffian spline functions. Journal of Mathematical Analysis and Applications, 33, 82-95.
7. Matić, N., Guyon, I., Denker, J., & Vapnik, V. (1993). Writer adaptation for on-line handwritten character recognition. International Conference on Document Analysis and Recognition. IEEE Computer Society Press.
8. Platt, J. C., & Matić, N. P. (1997). A constructive RBF network for writer adaptation. Advances in Neural Information Processing Systems 9. MIT Press.
9. Platt, J. (1998). Sequential minimal optimization: A fast algorithm for training support vector machines (Technical Report 98-14). Microsoft Research, Redmond, Washington.
10. Rowley, H. A., Goyal, M., & Bennett, J. (2002). The effect of large training set sizes on online Japanese kanji and english cursive recognizers. International Workshop on Frontiers in Handwriting Recognition.
11. Schölkopf, B., Herbrich, R., & Smola, A. J. (2001). A generalized representer theorem. Proceedings of the 14th Annual Conference on Computational Learning Theory (pp. 416-426). Springer Verlag.
12. Schölkopf, B., & Smola, A. J. (2002). Learning with kernels. MIT Press.
13. Shawe-Taylor, J., & Cristianini, N. (2004). Kernel methods for pattern analysis. Cambridge University Press.
14. Sollich, P. (2000). Probabilistic methods for support vector machines. Advances in Neural Information Processing Systems 12. MIT Press.